The MAC Job Board is an AI-powered job aggregation platform built for the Monash Association of Coding (MAC)—the largest student-led coding club in the Southern Hemisphere with over 100,000 followers across all social media platforms. We built a custom scraper in Go that automatically collects publicly available job data daily from various APIs and websites, then presents it through a lightning-fast Next.js frontend with intelligent AI-powered filtering.

In just one week since launch, we recorded over 2.2k active users with 10k page visits, achieving a Lighthouse performance score of 96. The platform helps Australian IT students find and track tech opportunities without needing to filter through multiple job-seeking websites themselves.

For Australian IT students looking for internships and graduate roles, the job search process is exhausting. You have to check Seek, LinkedIn, Indeed, Grad Connection, Prosple, company career pages, and countless other platforms daily. Each site has different filters, different formats, and you end up spending hours just trying to find relevant opportunities.

MAC has a massive community of students actively job hunting, but they were all experiencing this same pain. Jobs were also scattered across Discord messages, email chains, and social media posts from our sponsor companies like Atlassian and Jane Street. Students were missing opportunities simply because they didn't check the right platform at the right time.

We needed a centralized solution that would automatically aggregate relevant tech jobs from across the internet, filter them specifically for our Australian student audience, and present them in one clean, fast, easy-to-use interface.

We built a two-part system: a Go-based scraper that runs daily to collect job data, and a Next.js frontend that presents it all in a blazingly fast, beautifully designed interface.

The scraper uses various APIs and web scraping frameworks to pull publicly available job listings from major Australian job sites, company career pages, and sponsor postings. We specifically target tech roles relevant to students: internships, graduate programs, entry-level positions, and student opportunities.

On the frontend, we use AI to enhance the accuracy of job listings and provide smart filtering that actually understands what students are looking for. Instead of basic keyword matching, the system can understand context and role requirements to surface the most relevant opportunities.

The result is a platform where students can browse hundreds of curated, relevant tech jobs in one place, with powerful filters and a user experience that makes job hunting actually enjoyable. No more tab-hopping between different sites.

1. Daily Data Collection: Our Go scraper runs automated jobs to fetch listings from multiple sources using APIs and web scraping frameworks
2. AI Processing: Job listings are processed through AI models to enhance accuracy, categorize roles, and extract relevant skills and requirements
3. Smart Filtering: Students can filter by role type, location, company, skills, and more—with AI helping to match context beyond simple keywords
4. Lightning-Fast UI: Built with Next.js Server Components and Server Actions for instant page loads and snappy interactions (Lighthouse score: 96)
5. Sponsor Integration: Featured listings from club sponsors like Atlassian and Jane Street get highlighted visibility for our community

The architecture is split between a robust Go backend for data collection and a high-performance Next.js frontend. Here's how we achieved a Lighthouse score of 96 while handling thousands of job listings:

1. Go Scraper & Data Pipeline: Custom-built scraper using Go for high performance and concurrency. Uses various APIs and web scraping frameworks to collect publicly available job data daily, with error handling and retry logic for reliability
2. AI-Enhanced Processing: Job listings are processed through AI models to improve accuracy, standardize formats, extract skills, and provide intelligent categorization—making filtering far more effective than simple keyword matching
3. Next.js Server Components: We heavily utilize Server Components and Server Actions to minimize client-side JavaScript and achieve incredibly fast page loads. Most data fetching and processing happens on the server for optimal performance
4. TypeScript & Tailwind: Full TypeScript for type safety across the codebase, with Tailwind CSS for rapid UI development and consistent design. Responsive across all device sizes
5. Microsoft Azure Infrastructure: Thanks to Microsoft Azure Communities' non-profit grant, we power our servers and AI processing on Azure, giving us the compute resources needed to serve thousands of users reliably

The platform combines the performance of Go for backend data processing with the speed and developer experience of modern Next.js for the frontend. We prioritized technologies that would scale with our growing user base.

The job board is live at jobs.monashcoding.com, serving thousands of students across Australia.

We launched quietly with just an announcement to our community, no paid marketing, no aggressive promotion. The organic adoption blew us away.

The reception from the MAC community has been incredible. Students finally have a centralized place to find tech opportunities without spending hours filtering through irrelevant listings on multiple platforms. The AI-powered filtering genuinely works—students are finding roles that match their skills and interests much faster than traditional job boards.

For our sponsor companies like Atlassian and Jane Street, the platform provides a direct channel to reach a highly engaged, tech-savvy student audience. They can get their graduate and internship opportunities in front of thousands of qualified candidates who are actively job hunting.

This is just the beginning. The projects team is committed to continuously improving the job board—adding more sources, refining the AI filtering, and building features the community actually wants. The platform is live, actively maintained, and helping thousands of Australian students land their dream tech roles.